Systems | Development | Analytics | API | Testing

Lending has always been at the heart of banking. But the way loans originated is going through a quiet but powerful shift. Customers today expect instant decisions. Not in days. Not even in hours. They expect approvals in minutes, sometimes seconds. And they expect this experience to be smooth across mobile apps, web platforms, and embedded finance ecosystems. This is where the cracks in traditional systems start to show. Legacy platforms were never designed for this kind of speed or scale.

Building the foundation for AI with Fivetran and Google Cloud.

A five-question audit, with the exact prompts to paste, walked through on a real LinkedIn CPL spike.

We are launching with this post a new series of blog articles and LinkedIn posts titled "Features Sitting Idle". In this series, we explore key features of OctoPerf that are either misused, misunderstood, or simply unknown to our users. It's time to shine a light on these hidden gems, features that are already there, ready to become a central part of how you test. This is probably the most common situation after a load test.

The emergence of digital transformation technologies led to a nationwide change, causing a profound impact on various industries throughout the world. Among the conventional sectors affected by it, the healthcare industry emerged prominently. Interestingly, it not only disrupted but also provided a significant impetus to the healthcare sector, thereby positively influencing the different types of healthcare software and the medical software industry.

RESTful APIs and microservices solve different problems — REST is a style of API design, microservices is a pattern for structuring an application — but they work together so often that they're frequently confused. Most production microservices architectures use REST as the default communication mechanism between services, while plenty of monolithic applications also expose RESTful APIs to external clients.

This is Part 2 of the AI Software Factory series. In Part 1, we established that the Agile methodology is buckling under the weight of “elastic code.” When AI agents can generate functionality in seconds, two-week sprints and manual task management become organizational bottlenecks. We introduced the concept of the AI Software Factory: a shift from managing human tasks to managing business intent through a “Funnel of Increasing Trust.” But a factory requires infrastructure.

We’ve been building an AI agent that can take a production bug, find the root cause in captured traffic, write a fix, and validate it before a human reviews it. We call it Agent Factory. Last week we ran it on ourselves, against a real bug in our own production service. The first thing we did was get the workflow wrong.

How much of your CI runtime is spent waiting on APIs that return the same response every time? For most teams, it’s more than they realise. Mock testing cuts that wait to zero. Instead of calling real services, teams simulate the responses they need. Faster feedback, better isolation, and test runs that don’t fail because a payment sandbox was slow. But like most testing techniques, mocking works well only when used correctly.

Sandbox testing catches the failures that staging misses, and production makes expensive. Every team reaches a point where testing against real systems stops being practical. The payment gateway costs money per call. The third-party notification service has rate limits. One wrong database query corrupts shared test data and breaks everyone’s runs. A sandbox environment for testing gives you an isolated, controlled space where none of that matters.